Research on deep learning-based evidence analysis and judicial decision support in legal practice

Computational methods, based on scientific principles and experimental data, can give objective and quantitative analysis results, providing a key basis for the determination of traffic accident responsibility, medical disputes, industrial injury identification and the trial of complex cases such as criminal cases. In this paper, the basic concepts, application scenarios and its influence on judicial decision-making of computational evidence are systematically sorted out, and then DL technology is introduced to optimize the evidence analysis process. By building a data processing model based on deep learning (DL), this paper explores the potential of artificial intelligence in interpreting computational evidence. In this study, more than 500 cases involving computational evidence collected from 2015 to 2024 were analyzed quantitatively and qualitatively. The results show that the adoption rate of computational evidence in traffic accident cases is 82%, which greatly improves the success rate of the case and the rationality of the compensation amount. In criminal proceedings and medical disputes, this kind of evidence is also convincing and reliable. The application of DL technology can improve the efficiency of evidence processing and reduce subjective deviation.